DOI: 10.1016/j.jag.2014.03.002
Scopus记录号: 2-s2.0-84904479926
论文题名: Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland deepor beel in northeast India using a multi-layer perceptron neural network
作者: Mozumder C ; , Tripathi N ; K
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 32, 期: 1 起始页码: 92
结束页码: 104
语种: 英语
英文关键词: Deepor beel
; Land use modelling
; Multi-layer perceptron neural network
; Sensitivity analysis
; Urban growth
; Wetland conservation
Scopus关键词: artificial neural network
; computer simulation
; conservation management
; environmental impact
; environmental restoration
; land use planning
; Markov chain
; prediction
; sensitivity analysis
; sustainability
; upland region
; urban growth
; urban planning
; wetland
; zoning policy
; Assam
; Deepor Beel
; Gauhati
; India
英文摘要: In recent decades, the world has experienced unprecedented urban growth which endangers the green environment in and around urban areas. In this work, an artificial neural network (ANN) based model is developed to predict future impacts of urban and agricultural expansion on the uplands of Deepor Beel, a Ramsar wetland in the city area of Guwahati, Assam, India, by 2025 and 2035 respectively. Simulations were carried out for three different transition rates as determined from the changes during 2001 -2011, namely simple extrapolation, Markov Chain (MC), and system dynamic (SD) modelling, using projected population growth, which were further investigated based on three different zoning policies. The first zoning policy employed no restriction while the second conversion restriction zoning policy restricted urban-agricultural expansion in the Guwahati Municipal Development Authority (GMDA) proposed green belt, extending to a third zoning policy providing wetland restoration in the proposed green belt. The prediction maps were found to be greatly influenced by the transition rates and the allowed transitions from one class to another within each sub-model. The model outputs were compared with GMDA land demand as proposed for 2025 whereby the land demand as produced by MC was found to best match the projected demand. Regarding the conservation of Deepor Beel, the Landscape Development Intensity (LDI) Index revealed that wetland restoration zoning policies may reduce the impact of urban growth on a local scale, but none of the zoning policies was found to minimize the impact on a broader base. The results from this study may assist the planning and reviewing of land use allocation within Guwahati city to secure ecological sustainability of the wetlands. © 2014 Elsevier B.V. All rights reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79672
Appears in Collections: 气候变化事实与影响
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作者单位: Remote Sensing and CIS, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Khlong Luang, Pathumthani 12120, Thailand
Recommended Citation:
Mozumder C,, Tripathi N,K. Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland deepor beel in northeast India using a multi-layer perceptron neural network[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,32(1)